ISO 9001:2015 Risk-based Thinking: A Framework using Fuzzy

Makara J. Technol. 24/3 (2020), 149159

doi: 10.7454/mst.v24i3.3944

December 2020 | Vol. 24 | No. 3 149

ISO 9001:2015 Risk-based Thinking:

A Framework using Fuzzy-Support Vector Machine

Ralph Sherwin A. Corpuz

Electronics Engineering Technology, Technological University of the Philippines, Manila 1000, Philippines

*e-mail: [email protected]

Abstract

Risk-based thinking (RBT) is one of the distinct new features of the International Organization for Standardization

9001:2015. Interestingly, the standard does not prescribe any tools. Hence, organizations are puzzled as to the extent of

conformance. Some organizations have adopted formal tools. However, these tools seem insufficient in linking the

standard into an evidence-based decision support system. To resolve gaps in RBT implementation, this paper proposes a

framework based on fuzzy inference system (FIS) and support vector machine (SVM) to automate risk analysis and

evaluation, proposal and verification of action plans, and prediction of the feasibility of risks and opportunities according

to text patterns. Modeling results indicate that the framework has no significant difference in terms of accuracy compared

with the conventional method. Both FIS-1 and FIS-2 models, however, are statistically significantly faster at 3.26 and 1.15

s, respectively. Meanwhile, the SVM model, whose text classification features are not evident in the conventional method,

has a 97.16% classification accuracy and 2.6% confusion error during training, and 95% classification accuracy during

testing. Results affirm that FIS and SVM are efficient tools in feasibly conforming with the RBT requirements of the ISO

9001:2015 international standard.

Abstrak

Pemikiran Berbasis Risiko ISO 9001:2015: Suatu kerangka Kerja yang Menggunakan Mesin Vektor Pendukung

yang Kabur. Pemikiran berbasis risiko (Risk-based thinking (RBT)) merupakan salah satu dari fitur-fitur baru yang

berbeda dari Organisasi Internasional untuk Standarisasi 9001:2015 (International Organization for Standardization

9001:2015). Yang menarik, standar tidak menentukan perkakas apapun. Oleh karenanya, berbagai organisasi dibingungkan

dengan tingkat kesesuaian. Sebagian organisasi telah mengadopsi perkakas formal. Namun demikian, perkakas ini

nampaknya tidak mencukupi dalam menghubungkan standar ke dalam suatu sistem pendukung keputusan berbasis

kejadian. Untuk menyelesaikan adanya celah-celah di dalam implementasi RBT, naskah ini mengusulkan suatu kerangka

kerja berdasarkan pada sistem dugaan yang kabur (fuzzy inference system (FIS)) dan mesin vektor pendukung (support

vector machine (SVM)) untuk mengotomatisasi analisis risiko dan evaluasi, usulan dan verifikasi rencara aksi, dan prediksi

kelayakan risiko serta peluang yang sesuai dengan pola-pola naskah. Hasil-hasil pemodelan menunjukkan bahwa kerangka

kerja tersebut tidak memiliki perbedaan yang signifikan dalam hal akurasi dibandingkan dengan metode konvensional.

Namun demikian, baik model FIS-1 maupun FIS-2, secara statistik jauh lebih cepat pada masing-masing 3,26 dan 1,15

detik. Sementara, model SVM, yang fitur-fitur klasifikasi naskahnya bukan kejadian di dalam metode konvensional,

memiliki akurasi klasifikasi 97,16% dan kesalahan yang membingungkan 2,6% selama pelatihan, dan akurasi klasifikasi

95% selama pengujian. Hasil-hasilnya menegaskan bahwa FIS dan SVM merupakan perkakas yang efisien dengan

penyesuaian yang layak dengan persyaratan RBT dari standar internasional ISO 9001:2015.

Keywords: artificial intelligence, fuzzy inference system, ISO 9001:2015, risk-based thinking, support vector machine

1. Introduction

The ISO 9001 Quality Management Systems

Requirements (QMS) International Standard is the most

widely sought standard in the world used for the

attainment of certifications related to quality [1]. The

standard is constantly updated after years of review and

improvement to consistently meet the growing demands

of the global markets for quality products and services.

One of the new distinct features in the ISO 9001:2015

version is the introduction of risk-based thinking (RBT),

which is a conceptual framework that requires an

organization to understand its context through the

determination of internal and external issues and

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relevant interested parties; to determine the risks as a

basis for planning activities; to implement QMS

processes; to determine the extent of relevant

documented information; to evaluate and review the

effectiveness of actions to address risks and

opportunities; and to update the risks and opportunities

during planning if necessary [2].

A risk is defined as the effect of uncertainty, which can

either be negative or positive. Mentioned alongside risks

are the possibility of opportunities, which are a set of

circumstances that make it possible to do something

beneficial for an organization. In this context, RBT is

helpful in managing these risks and opportunities to

avoid nonconformities in the future [2]−[4]. To

complement the ISO 9001 requirements, ISO has

published the ISO 31000-Risk Management Principles

and Guidelines, the process map of which is illustrated

in Figure 1 [5] as a guide for organizations in managing

risks.

Interestingly, the standard offers greater flexibility in

implementing the RBT in terms of documented

information, scope of application, and on organizational

roles [2]. However, this flexibility causes confusion

among organizations, particularly with regard to the

extent of the minimum documentation and

implementation requirements [4], [6]. Most service-

oriented multinational organizations implement a formal

risk management process wherein they use traditional

tools aside from the ISO 31000, such as Total

Productive Maintenance, AS/NZS 4360 Risk

Management Standard, and Failure Modes and Effects

Analysis [4], [6]−[7]. Unfortunately, these tools are

incapable of capturing the uncertainties of risk

management process, which are usually expressed in

qualitative data and unrealistically show similar levels

of risk ratings despite different significance levels of

parameters [8].

Figure 1. ISO 31000 Risk Management Process

Artificial intelligence (AI) is an emerging field of

disruptive technologies explored for the design of data-

driven risk management tools. Fuzzy inference system

(FIS) and support vector machines (SVM) are among

the highly sought techniques for such purpose. FIS is a

popular AI technique introduced by Zadeh [9] and is

intended specifically as a measure of uncertainties,

vagueness, and imprecisions. Figure 2 [10] shows a

sample FIS process model, which has five basic

subprocesses. Initially, both input and output data are

modeled into certain membership functions (MF). The

resulting fuzzy sets are then applied with logical

operators whether to intersect (AND) or disjoint (OR)

and then implied to follow a rule-based system, which is

also known as fuzzy rules. Afterward, the resulting

fuzzy sets are aggregated and then finally defuzzified to

yield the desired outputs. FISs are used for

semiquantitative or qualitative types of prediction and

control systems [8], [10]−[11].

SVM is a popular AI technique postulated by Vapnik

[12] based on statistical principles of Huber’s regression

theory and Wolfe’s dual program theory [13]. Figure 3

shows a sample SVM model used for binary

classification [14]. SVM classifies data by finding the

best hyperplane that separates all data points of one

class from the other class. A hyperplane is considered

“best” if it has the largest “margin” between two classes.

The data points that are closest to the “separating

hyperplane” located in the boundary of the margin slab

are called “support vectors.” The “+” and “−” indicate

“type 1” and “type −1” data points, respectively. SVMs

have comparable or higher performance than traditional

learning techniques in terms of generalization ability,

Figure 2. FIS Process Model

Figure 3. SVM Binary Classification Model

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151

robustness, and prediction accuracy. Hence, they are a

popular choice for prediction and classification

applications [15]−[18].

Inevitably, FIS and SVM also have imperfections. FIS

is not efficient in purely quantitative measurements and

in large problems [15]. The same problem is found with

the SVM, which is dependent on its kernel optimization

and is susceptible to slow performance with large

datasets due to memory constraints [19]. In an effort to

resolve these issues, the fusion of FIS and SVM

techniques has opened the feasibility of complementing

their respective strengths and weaknesses to improve

machine learning performance. Extensive studies have

been conducted relative to the use of FIS and SVM,

collectively called fuzzy-SVM, for risk management

systems such as those applied for road safety [15], credit

risk evaluation [18], project risk assessment [19], and

fire risk assessment [20]. These studies, however, are

focused on enhancing optimization techniques, such as

improvement of MF or kernel parameters, and are not

intended as a practical solution to RBT problems. In

fact, there is a dearth of published research papers

related to the use of FIS and SVM for RBT. With that

said, this paper chronicled the design of a framework to

feasibly meet the documentation and implementation

requirements of the ISO 9001:2015 international

standard with respect to RBT. The author focused on

resolving gaps in data entry automation to ensure

efficiency, timeliness, and accuracy of monitoring,

measurement, analysis, and evaluation, and to facilitate

a predictive decision support system for top management

through the use of FIS and SVM techniques.

2. Methods

In this paper, the author utilized RBT data of a state-run

university, the Technological University of the

Philippines (TUP) in Manila, Philippines, as of March

2020. Figure 4 shows the actual RBT process observed

in the university, also known as internal and external

issues assessment procedure. Each office is represented

by a process owner (PO)—who is either a dean,

director, or head—who maintains an online log to

comply with the RBT requirements and updates the

contents at least once a year. However, the certifying

body found that this procedure should be simplified and

should have provisions to monitor the effectiveness of

actions to address risks and opportunities.

As a potential solution to the research gap, the proposed

RBT framework is shown in Figure 5. It is composed of

three complementary modules, namely, the (1) corpus

module, (2) FIS module, and (3) SVM) module. The

corpus module is a collection of textual statements of

issues “I”, management controls “C”, and risks “Ri” and

opportunities “Op.” Table 1 shows a sample corpus

dataset predetermined by a concerned PO.

Figure 4. Sample RBT Process

Figure 5. Proposed RBT Framework

Table 2. Sample RBT Corpus Data

I C

Outdated instructional

tools and equipment

Level 4-Conduct

periodic follow up with

department heads

Ri Op

Inadequate knowledge of

new technologies and

their applications

Trainings and workshops

for teachers

The FIS module was designed using MATLAB with

three distinct objectives: (1) to analyze risks “R” and

opportunities “O”; (2) to determine action plans to

manage risks “RA” and opportunities “OA”; and (3) to

evaluate the effectiveness of action plans to address

risks “RE” and opportunities “OE”. To realize these

objectives, the author utilized equations to standardize

the input data and then modeled two Mamdani-type FIS

models known as FIS-1model, to cater to objectives 1

and 2, and FIS-2 model, to cater to objective 3.

Initially, each PO was required to determine the

equivalent numerical levels of “R” and “O” by rating

the specific parameters by using a five -point scale

criteria. The following formulas were used to quantify

the values of “R,” “O,” and their respective parameters:

𝑅 = 𝑃𝑅𝑆 (1)

where R is the level of risk, and PR is the average

probability and S is the average severity, respectively,

which are further expressed using the following

equations (2 and 3), respectively:

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𝑃𝑅 = [𝑃𝑅1+𝑃𝑅2

2] (2)

𝑆 = [𝑆1+𝑆2+𝑆3+𝑆4+𝑆5+𝑆6

6] (3)

With the use of the five-point scale criteria, “PR1”

(previous risk probability) and “PR2” (future risk

probability) were rated as 1 = improbable; 2 = remote;

3 = occasional; 4 = probable; 5 = frequent; and “S1”

(inability to meet customer requirements), “S2”

(potential violation of statutory requirements), “S3”

(potential violation of regulatory requirements), “S4”

(potential violation of organizational policies), and “S5”

(potential impact on organizational reputation) were

rated as 1 = N/A or negligible; 2 = minor; 3 = serious;

4 = critical; 5 = catastrophic; while “S6” (estimated cost

of correction) was rated as 1 = 0 or N/A;

2 = <PhP100,000; 3 = PhP100,001–500,000;

4 = PhP500,001–1,000,000; 5 = >PhP1M.

Afterwards, the PO analyzed the values of “O” by using

the following equation:

𝑂 = 𝑃𝑂𝐵 (4)

where O is the level of opportunity. PO (average

probability) and B (average benefits) are expressed

further in equations (5) and (6), respectively

𝑃𝑂 = [𝑃𝑂1+𝑃𝑂2

2] (5)

𝐵 = [𝐵1+𝐵2+𝐵3+𝐵4+𝐵5+𝐵6

6] (6)

With the use of a five-point scale criteria, “PO1”

(previous opportunity probability) and “PO2” (future

opportunity probability) were rated as 1 = improbable;

2 = remote; 3 = occasional; 4 = probable; 5 = frequent;

and “B1” (potential for new business/products

/services), “B2” (potential for organizational expansion),

“B3” (potential for satisfying regulations), “B4”

(potential for the improvement of QMS processes), and

“B5” (potential improvement of organizational

reputation) were rated as 1 = none/N/A; 2 = minor;

3 = moderate; 4 = high; 5 = very high; while “B6”

(estimated cost of implementation) was rated as

1 = >PhP1M; 2 = PhP500,001–1,000,000;

3 = PhP100,001–500,000; 4 = <PhP100,000; 5 = 0 or

N/A.

The values of “R” and “O”, including their respective

parameters “PR,” “S,” “PO,” and “B,” were evaluated

twice a year. Hence, the notation of subscripts “A” and

“B” was used to indicate the first and second period

ratings, respectively. The following equations were used

to indicate the specific period ratings:

𝑅𝐴 = 𝑃𝑅𝐴𝑆𝐴 (7)

𝑅𝐵 = 𝑃𝑅𝐵𝑆𝐵 (8)

𝑃𝑅𝐴 = [𝑃𝑅1𝐴+𝑃𝑅2𝐴

2] (9)

𝑃𝑅𝐵 = [𝑃𝑅1𝐵+𝑃𝑅2𝐵

2] (10)

𝑆𝐴 = [𝑆1𝐴+𝑆2𝐴+𝑆3𝐴+𝑆4𝐴+𝑆5𝐴+𝑆6𝐴

6] (11)

𝑆𝐵 = [𝑆1𝐵+𝑆2𝐵+𝑆3𝐵+𝑆4𝐵+𝑆5𝐵+𝑆6𝐵

6] (12)

𝑂𝐴 = 𝑃𝑂𝐴𝐵𝐴 (13)

𝑂𝐵 = 𝑃𝑂𝐵𝐵𝐵 (14)

𝑃𝑂𝐴 = [𝑃𝑂1𝐴+𝑃𝑂2𝐴

2] (15)

𝑃𝑂𝐵 = [𝑃𝑂1𝐵+𝑃𝑂2𝐵

2] (16)

𝐵𝐴 = [𝐵1𝐴+𝐵2𝐴+𝐵3𝐴+𝐵4𝐴+𝐵5𝐴+𝐵6𝐴

6] (17)

𝐵𝐵 = [𝐵1𝐵+𝐵2𝐵+𝐵3𝐵+𝐵4𝐵+𝐵5𝐵+𝐵6𝐵

6] (18)

The author designed the FIS module by using

MATLAB. Figure 6 shows the two combined Mamdani-

type FIS-1 models intended to realize objectives 1 and 2

and the FIS-2 model used to achieve objective 3 of the

study. As shown, FIS-1 model is composed of 4 input

data for “R,” namely, “PRA,” “SA,” “PRB,” and “SB”;

another 4 input data for “O,” namely, “POA,” “BA,”

“POB,” and “BB”; 2 output data for “R,” namely, “RA-

RAA,” “RB-RAB”; and 2 output data for “O,” namely,

“OA-OAA” and “OB-OAB.”

To characterize the input and output parameters of the

FIS-1 model, the author designed their respective MF,

as elaborated in Table 2. A sample representation of an

input data “PRA” is further elucidated in Figure 7.

Figure 6. FIS-1 and FIS-2 Models



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Table 2. MF for Risks and Opportunities

Data/Role MF Type Parameters Definition/Action Plan

PRA, PRB, POA,

POB

(Input)

Gaussmf [0.25 1] Improbable

[0.25 2] Remote

[0.25 3] Occasional

[0.25 4] Probable

[0.25 5] Frequent

SA, SB, BA, BB

(Input)

Gbellmf [0.25 2.5 1] Negligible/None

[0.25 2.5 2] Minor

[0.25 2.5 3] Serious/Moderate

[0.25 2.5 4] Critical/High

[0.25 2.5 5] Catastrophic/Very High

RA-RAA, RB-RAB

(Output)

Trimf [0 2.5 5] Very Low – Take Risk in Order to Pursue an Opportunity

(VL-TRIOTPAO)

[5.01 7.5 10] Low – Retain Risk by Informed Decision (L-RRBID)

[10.01 12.5 15] Medium – Change Probability or Severity (M-CPOS)

[15.01 17.5 20] High – Eliminate the Risk Source (H-ETRS)

[20.01 22.5 25] Very High – Avoid the Risk (VH-ATR)

OA-OAA, OB-OAB Trimf [0 2.5 5] Very Low – Reject Opportunity Outright (VL-ROO)

[5.01 7.5 10] Low – Consider Opportunity for Further Decisions (L-

COFFD)

[10.01 12.5 15] Medium – Accept Opportunity Under Controlled

Conditions (M-AOUCC)

[15.01 17.5 20] High – Explore in Greater Details Before Pursuing (H-

EGDBP)

[20.01 22.5 25] Very High – Pursue the Opportunity (VH-PTO)

Table 3. MF for Effectiveness of Action Plans

Data/Role MF Type Parameters Action Plan/Effectiveness Level

RA-RAA, RB-RAB

(Input)

Gbellmf [1.25 2.5 2.5] Very Low – Take Risk in Order to Pursue an

Opportunity (VL-TRIOTPAO)

[1.245 2.501 7.5] Low – Retain Risk by Informed Decision

(L-RRBID)

[1.245 2.501 12.5] Medium – Change Probability or Severity

(M-CPOS)

[1.246 2.501 17.5] High – Eliminate the Risk Source (H-ETRS)

[1.245 2.501 22.5] Very High – Avoid the Risk (VH-ATR)

OA-OAA, OB-OAB

(Input)

Gaussmf [1.061 2.5] Very Low – Reject Opportunity Outright

(VL-ROO)

[1.061 7.5] Low – Consider Opportunity for Further Decisions

(L-COFFD)

[1.061 12.5] Medium – Accept Opportunity Under Controlled

Conditions (M-AOUCC)

[1.061 17.5] High – Explore in Greater Details Before Pursuing

(H-EGDBP)

[1.061 22.5] Very High – Pursue the Opportunity

(VH-PTO)

RE, OE Trimf [-46.15 -37.55 -28.94] Very Not Effective (VNE)

[-28.57 -19.29 -10] Not Effective (NE)

[-9.524 0 9.524] Moderately Effective (ME)

[9.999 19.35 28.57] Effective (E)

[28.94 37.55 46.15] Very Effective (VE)

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Afterward, the author established the relationship

between the input and output data through 100 sets of

fuzzy rules. The extent of programming the maximum

number of rules was based on the possible logical

combination of inputs vis-à-vis the desired output

response. Each rule has a pair of an antecedent (“if”), a

consequent (“then”), and with a total weight of “1.”

Figure 8 shows a screenshot of sample fuzzy rules (i.e.,

95–100) verbosely coded for risks, opportunities, and

action plans of the FIS-1 model.

Meanwhile, the FIS-2 model was designed with 2 input

data “RA-RAA” and “RB-RAB” for “R” and 2 input data

“OA-OAA” and “OB-OAB” for “O.” It also has 1 output

data “RE” and 1 output data “OE.” Table 3 summarizes

the MF of the input and output data of the FIS-2 model,

while Figure 9 details a sample MF of output data “RE.”

Figure 7. MF-PRA Input Data

Figure 8. FIS-1 Fuzzy Rules

Figure 9. MF- RE Output Data

Figure 10. FIS-2 Fuzzy Rules

Subsequently, the FIS-2 model was programmed with

50 sets of fuzzy rules to accurately determine the level

of effectiveness of “RE” and “OE.” Figure 10 shows a

screenshot of the 50 fuzzy rules (i.e., 45–50) of the FIS-

2 model.

After modeling, the author analyzed 20% of 211

datasets as sample test data and then compared the

performance of the FIS-1 and FIS-2 models with that of

the conventional manual method by using Google

Sheets, where the accuracy and timeliness of both

methods were noted. The comparison results were

analyzed by using paired sample t-test with 95%

confidence level and 5% percentage error on the basis

of the following formulas [10,21]:

H0:μ1=μ2 (19)

𝑡 =�̅�𝑑𝑖𝑓𝑓−0

𝑆�̅� (20)

𝑆�̅� =𝑆𝑑𝑖𝑓𝑓

√𝑛 (21)

where H0 is the null hypothesis; μ1 is the population

mean of the first variable; μ2 is the population mean of

the second variable; t is the test statistic; “x̅diff” is the

sample mean of the differences; n is the sample size;

Sdiff is the sample standard deviation of the differences;

and Sx̅ is the estimated standard error of the mean “S

√n.”

The last module designed was the SVM module, which

was designed by using MATLAB. This module was

used to analyze the text patterns of the previously stated

“Ri” and “Op” in the corpus module. The corresponding

“RE” and “OE” of the FIS-2 model were used as a

reference in defining the feasibility “F,” which was

precomputed by using the following formula and further

interpreted in Table 4.

𝐹 = [𝑅𝐸+𝑂𝐸

2] (22)

In the SVM module, the author loaded and extracted the

input and output data taken from the corpus module,

which were composed of 422 input data “Ri_Op” and

output data “F.” The input data were initially

preprocessed through tokenization or collection of

words for text analysis; filtering of stop words; stemming

Table 4. Feasibility of Risks and Opportunities

Level of Effectiveness Feasibility Interpretation

>+28.94 Very Feasible (VF)

+9.999 to +28.93 Feasible (F)

>−-9.524 to +9.998 Moderately Feasible (MF)

>-28.57 to -9.523 Not Feasible (NF)

>-46.15 to -28.56 Very Not Feasible (VNF)



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Figure 11. Tokenized Documents

Figure 12. Bag-Of-Words Model Results

Figure 13. ECOC SVM Classification Model Algorithm

or lemmatization to normalize the texts; removal of

punctuations, short words (<2 characters), and long

words (>15 characters); and the bag-of-words model.

Figures. 11 and 12 show the results of the preprocessing

steps, which resulted in 422 tokenized documents and a

total of 994 vocabularies.

Afterward, the author designed a text classification

model by using a compact version of the multi-class

error correcting output codes (ECOC) for SVM binary

learners with a one-versus-one coding design. Figure 13

shows the algorithm used to train the SVM model in a

supervised learning environment. It employed word

frequency counts of the bag-of-words model “Ri_Op”

as predictors and the feasibility levels “F” as the

response.

After the training, the author evaluated the performance

of the model in terms of classification accuracy “acc,”

which is the proportion of the labels that the model

predicted correctly, by using the following formula:

𝑎𝑐𝑐 = (𝑇𝑃 + 𝑇𝑁)

(𝑇𝑃 + 𝐹𝑃 + 𝐹𝑁 + 𝑇𝑁) (23)

where TP is the number of true positives; TN is the

number of true negatives; FP is the number of false

positives; and FN is the number of false negatives. The

“acc” was further validated using a confusion matrix

wherein the classification error, was determined by the

following equation:

𝐶𝐸 = ∑ 𝑤𝑗𝑒𝑗

𝑛𝑗=1

∑ 𝑤𝑗𝑛𝑗=1

(24)

where wj is the weight for the observation j, which is

normalized by the software to sum to 1; and ej = 1 if the

predicted class of observation j differs from its true

class, and 0 otherwise [22].

The author then simulated the model to test its

classification performance by using 20 textual

statements of “Ri” and “Op”. Similarly, these test data

were preprocessed and then further analyzed by using

paired sample t-test to determine if a significant

difference exists between the target and output classes.

3. Results and Discussion

The proposed three-module framework generally aimed

to document the requirements of the ISO 9001:2015

standard and to improve the efficiency of data collection

and decision-making through AI techniques.

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Figure 14. Corpus Module

(a) FIS-2 Input Data

(b) FIS-2 Output Data

Figure 15. FIS-1 Rule Viewer Results

(a) FIS-2 Input Data

(b) FIS-2 Output Data

Figure 16. FIS Rule Viewer Results

The corpus module was designed as a dynamic

collection of textual statements of issues “I,” current

controls “C” to mitigate the issues, and the risks “Ri”

and opportunities “Op.” In this study, the author

analyzed raw and unstructured 211 text-based data sets

as shown on Figure 14. “I” and “C” were used as

references only, while “Ri” and “Op” were practically

used in the succeeding SVM module. These text data

were initially identified by concerned PO. Hence, the

contents could be changed over time.

The FIS module was made up of the FIS-1 model used

to automate the analysis of “Ri” and their action plans

“RA-RAA” and “RB–RAB,” as well as “Op” and their

action plans “OA-OAA” and “OB-OAB”; and the FIS-2

model to evaluate the effectiveness of action plans “RE”

and “OE.” The modeling results are illustrated in the

rule and surface viewers of the MATLAB computing

software. Figure 15 shows a sample rule viewer of the

FIS-1 model, which illustrates how the model generates

the output data depending on the combination of input

data. In this particular example, if the input values are

PRA = 4.05 (probable-to-frequent), SA = 3.35 (serious-

to-critical), PRB = 2.25 (remote-to-occasional),

SB = 2.232 (minor-to-serious), POA = 0 (improbable),

BA = 0 (none), BB = 0 (none) and POB = 0 (none); then

the output values are RA-RAA = 12.8 (medium), RB-

RAB = 2.75 (very low), OA-OAA = 2.5 (very low), and

OB-OAB = 2.5 (very low).

Figure 16 shows a sample rule viewer of the FIS-2

model. In this particular example, if the input values are

RA-RAA = 12.8 (medium), RB-RAB = 2.75 (very low),

OA-OAA = 2.5 (very low), and OB-OAB = 2.5 (very

low), then the output data are RE = 37.4 (very effective;

VE) and OE = −6.57 (moderately effective).

The following surface viewers, which are 3D-based

graphical user interfaces, map out the relationship

between 2 input and 1 output data one calculation at a

time. Figure 17-a shows the surface viewer for the FIS-1

model, which shows inputs PRA = 4.05 (probable-to-

frequent) and SA = 3.35 (serious-to-critical) and the

output RA-RAA = 12.8 (medium). Meanwhile, Figure

17-b shows the surface viewer for the FIS-2 model with

inputs RA-RAA = 12.8 (medium) and RB-RAB = 2.75

(very low), and the output RE = 37.4 (VE). These maps

indicate the extent of the direct or inverse relationship

between 2 inputs and 1 output variable in a particular

period of analysis.



157

After modeling, the FIS module was tested to evaluate

its accuracy vis-à-vis the performance of the

conventional method. Table 5 shows the results of

paired samples t-test for the FIS-1 model. It reveals that

no significant difference exists in the accuracy between

the two methods, with t and p values of t1(42) = 1.355,

t2(42) = .443, t3(42) = 1.431, and t4(42) = 1.775 with

p1 = .183, p2 = .660, p3 = .160, and p4 = .083.

Interestingly, as elaborated in Table 6, the study found a

statistically significant improvement in the timeliness of

the computation in all test cases from 5.49 s ± 1.099 s to

2.23 s ± .782s (p = .000) for RAA, from 5.47 s ± 1.241 s

to 2.14 s ± .804 s (p = .000) for RAB, from 5.40 s ± 1.11

6 s to 2.21 s ± .742 s (p = .000) for OAA, and from 5.33

s ± 1.169 s to 2.09 s ± .895 s (p = .000) for OAB.

(a) FIS-1 (b) FIS-2

Figure 17. Sample FIS Surface Viewer Results

Subsequently, the FIS-2 model was tested to establish

its accuracy and timeliness in determining the “RE” and

“OE” in comparison with the conventional method. The

results of paired sample t-test, as shown in Table 7,

indicate that no significant difference exists in the

accuracy between the two methods, with t-values of

t1(42) = .443 and t2(42) = −.530, p1 = .660, and p2 =

.599.

Furthermore, Table 8 shows the inferential results of

paired sample t-test on the timeliness performance of

the FIS-2 model. The results reveal a statistically

significant improvement in the timeliness of

computation in all test cases from 2.65 s ± .482 s to 1.44

s ± .782 s (p = .000) for RE, and from 2.51 s ± .506 s to

1.42 s ± .499 s (p = .000) for OE.

Lastly, the SVM module was designed as a decision

support system that would be capable of analyzing the

text patterns of previously identified “Ri” and “Op” and

predicting the feasibility “F” of future proposals based

on related RBT parameters. The training results show

that the SVM model has a 97.16% classification

accuracy, as coded in Figure 18, and a confusion error

rating of 2.6%, as shown in the confusion matrix in

Figure 19.

Table 5. FIS-1 Accuracy Paired Sample T-Test

Mean Std.

Deviation

Std. Error

Mean

95% Confidence Interval

of the Difference t df Sig. (2-tailed)

Lower Upper

Pair 1 RAA

FIS1_RAA .070 .338 .052 -.034 .174 1.355 42 .183

Pair 2 RAB

FIS1_RAB .023 .344 .052 -.083 .129 .443 42 .660

Pair 3

OAA

FIS1_OA

A

.047 .213 .032 -.019 .112 1.431 42 .160

Pair 4 OAB

FIS1_OAB .070 .258 .039 -.010 .149 1.775 42 .083

Table 6. FIS-1 Timeliness Paired Sample T-Test

Mean Std.

Deviation

Std. Error

Mean



Lower Upper

Pair 1 RAA

FIS1_RAA 3.256 1.449 .221 2.810 3.702 14.734 42 .000

Pair 2 RAB

FIS1_RAB 3.326 1.443 .220 2.882 3.770 15.114 42 .000

Pair 3 OAA

FIS1_OAA 3.186 1.258 .192 2.799 3.573 16.602 42 .000

Corpuz


158

Table 7. FIS-2 Accuracy Paired Samples T-Test

Mean Std.

Deviation

Std.

Error

Mean



Lower Upper

Pair 1 RE

FIS2_RE .047 .688 .105 -.165 .258 .443 42 .660

Pair 2 OE

FIS2_OE -.093 1.151 .176 -.447 .261 -.530 42 .599

Table 8. FIS-2 Timeliness Paired Sample T-Test

Mean Std.

Deviation

Std. Error

Mean



Lower Upper

Pair 1 RE

FIS2_RE 1.209 .742 .113 .981 1.438 10.689 42 .000

Pair 2 OE

FIS2_OE 1.093 .684 .104 .883 1.303 10.485 42 .000

Figure 18. Training Classification Accuracy Results

Figure 19. Training Confusion Matrix Results

After being trained, the SVM model was implemented

and tested using actual 20 datasets. Figure 20 shows a

sample algorithm used to test the SVM model, while

Figure 21 shows the resulting output. The results

indicate that the SVM model was able to predict 19 out

of 20 actual datasets, thereby achieving a 95%

classification accuracy rating during testing.

Figure 20. Testing Algorithm

Figure 21. Results of Testing Algorithm

4. Conclusion

After simulation and statistical testing, the proposed

framework was found to have no significant difference

in terms of accuracy as compared with the conventional

method. However, both FIS-1 and FIS-2 models are

statistically significantly faster than the conventional

method by an average of 3.26 and 1.15 s, respectively.

Moreover, the SVM module has 97.16% classification

accuracy and 2.6% confusion error rating during

training, and an actual classification accuracy of 95%

FM

F NF V

FVNF

Target Class

F

MF

NF

VF

VNF

Ou

tpu

t C

lass

Confusion Matrix

64

15.2%

0

0.0%

0

0.0%

2

0.5%

0

0.0%

97.0%

3.0%

0

0.0%

206

48.8%

0

0.0%

4

0.9%

0

0.0%

98.1%

1.9%

1

0.2%

1

0.2%

40

9.5%

0

0.0%

0

0.0%

95.2%

4.8%

2

0.5%

0

0.0%

0

0.0%

90

21.3%

0

0.0%

97.8%

2.2%

0

0.0%

1

0.2%

0

0.0%

0

0.0%

11

2.6%

91.7%

8.3%

95.5%

4.5%

99.0%

1.0%

100%

0.0%

93.8%

6.2%

100%

0.0%

97.4%

2.6%



159

during testing. The strength of the SVM module is its

text classification feature for predicting text patterns of

risks and opportunities and their parameters; this ability

is not evident in existing conventional RBT systems.

Hence, all the results affirm that the use of FIS and

SVM is a feasibly efficient approach in designing an

RBT framework in conformance with the requirements

of ISO 9001:2015. Future research should focus on

increasing the RBT data collected from other similar

organizations to enhance the external validity of the

proposed framework.

Acknowledgements

The author would like to thank the support of the top

management and all POs toward the continuous

improvement of the QMS of TUP and the efforts

exerted by the quality assurance staff during the data

collection.

References

[1] R.S.A. Corpuz, Int. J. Recent Technol. Eng. 8

(2019) 420.

[2] International Organization for Standardization, ISO

9001:2015 Quality management systems

requirements, 2015.


9001:2015 Fundamentals and vocabulary, 2015.

[4] A.Y. Ezrahovich, A.V. Vladimirtsev, I.I. Livshitz,

P.A. Lontsikh, V.A. Karaseva, International

Conference Quality Management, Transport and

Information Security, Information Technologies,

St. Petersburg, Russia, 2017, p. 506.


31000:2009 Risk management: principles and

guidelines, 2015.

[6] T. Aven, Risk Anal. 32 (2012) 1647.

[7] H. Pačaiová, J. Sinay, A. Nagyová, Meas. 100

(2017) 288.

[8] M. Gallab, H. Bouloiz, Y. L. Alaoui, M. Tkiouat,

Second International Conference on Intelligent

Computing in Data Sciences (ICDS), Fez,

Morocco, 2019, p. 226.

[9] Z. Zadeh, Comput., 1 (1988) 83.

[10] A.B. Villanueva, R.S.A. Corpuz, Int. J. Sci.

Technol. Res. 9 (2020) 2096.

[11] L. Pokoradi, AARMS. 1 (2002) 63.

[12] V. Vapnik, The Nature of Statistical Learning

Theory (2nd ed), Springer, Berlin, 1999.

[13] W. Feng, Y. Xiao, Pacific-Asia Conference on

Circuits, Communication and System, Chengdu,

China, 2009, p. 710.

[14] T. Hastie, R. Tibshirani, J. Friedman. The Elements

of Statistical Learning, second edition. Springer,

New York, 2008.

[15] M.Y. Cheng, A.F.V. Roy, K.L. Chen, Expert Syst.

Appl. 39 (2012) 1737.

[16] C. Burges, Data Min. Knowl. Discov. 2 (1998)

121.

[17] Y. Wang, S. Wang, K.K. Lai, IEEE Trans. Fuzzy

Syst. 13 (2005) 820.

[18] P.Y. Hao, M-S. Lin, L-B. Tsai, 8th International

Conference on Intelligent Systems Design and

Applications, Vellore, India, 2008, p. 83.

[19] Z. Jia, L. Gong, J. Han, International Conference

on Computer Science and Software Engineering,

Wuhan, China, 2008, p. 508.

[20] C.K. Lau, K.K. Lai, Y.P. Lee, J. Du, Fire Saf. J. 28

(2015) 188.

[21] R.S.A. Corpuz, J. C. Orquiza, 10th International

Conference on Humanoid, Nanotechnology,

Information Technology, Communication and

Control, Environment and Management (HNICEM),

Baguio City, Philippines, 2018,

[22] MathWorks Inc., Classification ECOC Loss,

https://www.mathworks.com/help/stats/classificatio

necoc.loss.html, 2020.